docs: remove converted MD-files add evolution

README.md

@@ -135,19 +135,19 @@ You can also specify batch size with `-b` or `--batch-size` parameter.
 
 | Component | Description |
 | --------- | ----------- |
-| [NER component](deeppavlov/models/ner/README.md) | Based on neural Named Entity Recognition network. The NER component reproduces architecture from the paper [Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition](https://arxiv.org/pdf/1709.09686.pdf) which is inspired by Bi-LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf. |
-| [Slot filling components](deeppavlov/models/slotfill/README.md) | Based on fuzzy Levenshtein search to extract normalized slot values from text. The components either rely on NER results or perform needle in haystack search.|
+| [NER component](docs/userdocs/ner.rst) | Based on neural Named Entity Recognition network. The NER component reproduces architecture from the paper [Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition](https://arxiv.org/pdf/1709.09686.pdf) which is inspired by Bi-LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf. |
+| [Slot filling components](docs/userdocs/slot_filling.rst) | Based on fuzzy Levenshtein search to extract normalized slot values from text. The components either rely on NER results or perform needle in haystack search.|
 | [Classification component](docs/userdocs/classifiers.rst) | Component for classification tasks (intents, sentiment, etc) on word-level. Shallow-and-wide CNN, Deep CNN, BiLSTM,  BiLSTM with self-attention and other models are presented. The model allows multilabel classification of sentences. |
-| [Goal-oriented bot](deeppavlov/models/go_bot/README.md) | Based on Hybrid Code Networks (HCNs) architecture from [Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017](https://arxiv.org/abs/1702.03274). It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can switched on and off on demand.  |
-| [Seq2seq goal-oriented bot](deeppavlov/models/seq2seq_go_bot/README.md) | Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. |
-| [Automatic spelling correction component](deeppavlov/models/spelling_correction/README.md) | Pipelines that use candidates search in a static dictionary and an ARPA language model to correct spelling errors. |
+| [Goal-oriented bot](docs/userdocs/go_bot.rst) | Based on Hybrid Code Networks (HCNs) architecture from [Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017](https://arxiv.org/abs/1702.03274). It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can switched on and off on demand.  |
+| [Seq2seq goal-oriented bot](docs/userdocs/seq2seq_go_bot.rst) | Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. |
+| [Automatic spelling correction component](docs/userdocs/spelling_correction.rst) | Pipelines that use candidates search in a static dictionary and an ARPA language model to correct spelling errors. |
 | [Ranking component](docs/userdocs/neural_ranking.rst) |  Based on [LSTM-based deep learning models for non-factoid answer selection](https://arxiv.org/abs/1511.04108). The model performs ranking of responses or contexts from some database by their relevance for the given context. |
 | [Question Answering component](docs/userdocs/squad.rst) | Based on [R-NET: Machine Reading Comprehension with Self-matching Networks](https://www.microsoft.com/en-us/research/publication/mrc/). The model solves the task of looking for an answer on a question in a given context ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) task format). |
-| [Morphological tagging component](deeppavlov/models/morpho_tagger/README.md) | Based on character-based approach to morphological tagging [Heigold et al., 2017. An extensive empirical evaluation of character-based morphological tagging for 14 languages](http://www.aclweb.org/anthology/E17-1048). A state-of-the-art model for Russian and several other languages. Model assigns morphological tags in UD format to sequences of words.|
+| [Morphological tagging component](docs/userdocs/morphotagger.rst) | Based on character-based approach to morphological tagging [Heigold et al., 2017. An extensive empirical evaluation of character-based morphological tagging for 14 languages](http://www.aclweb.org/anthology/E17-1048). A state-of-the-art model for Russian and several other languages. Model assigns morphological tags in UD format to sequences of words.|
 | **Skills** |  |
 |[ODQA](docs/userdocs/odqa.rst) | An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.|
 | **Parameters Evolution** |  |
-| [Parameters evolution for models](deeppavlov/models/evolution/README.md) | Implementation of parameters evolution for DeepPavlov models that requires only some small changes in a config file. |
+| [Parameters evolution for models](docs/userdocs/parameters_evolution.rst) | Implementation of parameters evolution for DeepPavlov models that requires only some small changes in a config file. |
 | **Embeddings** |  |
 | [Pre-trained embeddings for the Russian language](docs/userdocs/pretrained_vectors.rst) | Word vectors for the Russian language trained on joint [Russian Wikipedia](https://ru.wikipedia.org/) and [Lenta.ru](https://lenta.ru/) corpora. |